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The Role We are looking for an Applied Scientist / Applied ML Engineer to design, build, and deploy machine learning models that power pricing, bidding, and decisioning on a cross-border payments platform. This role owns problems end to end, from formulation to production, and partners closely with Product and Backend Engineering. Key Responsibilities 1. End-to-End ML Ownership - Own end-to-end ML solutions for pricing, bidding, and risk decisioning. - Formulate model objectives from first principles, including loss functions, constraints, and metrics, and implement them as production-grade services. - Experimentation & Iteration - Design and run experiments, including A/B tests and offline evaluations, and iterate with clear success metrics. - Production Monitoring - Monitor models in production, investigate regressions, and continuously improve performance. Requirements Essential - 3-7 years of experience as an ML Engineer, Applied Scientist, or Data Scientist in industry. - Bachelor's or Master's in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience. - Strong Python skills, including pandas, NumPy, and scikit-learn, plus at least one of PyTorch, TensorFlow. - Strong ML fundamentals, including supervised and unsupervised learning, model evaluation, regularization, feature engineering, and statistics. - Experience designing models from first principles and shipping them to production, in batch or real-time. - Hands-on experience with data pipelines and ETL, such as Airflow or Spark, and strong SQL for feature engineering. - Experience integrating ML into REST or gRPC APIs and microservice architectures. - Ability to design and interpret experiments with statistical rigor. - Solid problem-solving and communication skills, and the ability to work effectively in cross-functional and distributed teams. Nice to Have - Optimization, bandits, or decision-making under uncertainty, including dynamic pricing and bid optimization. - Bidding, auctions, marketplace, or recommendation systems experience. - Fintech background, including payments, cross-border, lending, trading, or risk and scoring. - Fraud, AML, credit risk, or vendor risk scoring models. - Model explainability tooling, including SHAP and feature importance, for auditable decisions. - Cloud experience (AWS, GCP, or Azure), Docker, and MLOps basics such as model registry and CI/CD. What We Offer - Real ML in production with direct impact on pricing, risk, and vendor decisions at scale. - Ownership of core models with room to influence architecture and roadmap. - Strong engineering peers and complex optimization problems in a high-growth fintech. Equal Opportunities Statement Tolken is an equal opportunity employer. We are committed to creating an inclusive environment for all employees. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.